We use the global matching feature map \(f_0^a\)  of \(I_0\)  and local matching features \({f}^{\mathcal {S}}\)  of \(\mathcal {I}^\mathcal {S}\)  to build a cost volume. The cost (or matching) volume is defined on a 3D view frustum attached to the camera in perspective projection. It is generated by running the traditional plane-sweep stereo [1]}, [2]} which uniformly samples \(M_0\) =64 plane hypotheses in the inverse depth space, s.t. \( 1/d \sim U(d_{\text{min}}, d_{\text{max}})\) . Here \(d_{\text{min}}\)  and \(d_{\text{max}}\)  are the near and far planes of the 3D frustum, respectively. Following [3]}, we set \(d_{min}\) =0.25 and \(d_{max}\) =20 meters for indoor scenes, e.g., ScanNet [4]}.
